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Hasan A. H. Naji
School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China

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Journal article
Published: 19 April 2020 in Sensors
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Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver’s age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.

ACS Style

Hasan A.H. Naji; Qingji Xue; Ke Zheng; Nengchao Lyu. Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model. Sensors 2020, 20, 2331 .

AMA Style

Hasan A.H. Naji, Qingji Xue, Ke Zheng, Nengchao Lyu. Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model. Sensors. 2020; 20 (8):2331.

Chicago/Turabian Style

Hasan A.H. Naji; Qingji Xue; Ke Zheng; Nengchao Lyu. 2020. "Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model." Sensors 20, no. 8: 2331.

Conference paper
Published: 01 October 2019 in Journal of Physics: Conference Series
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Service composition process generates composite services in order to fullfill service consumer's requirements that cannot be satisfied by a single service. Literature review addressed services composition but ignored verifying the existence of several serious issues, which may affect Service composition and may lead to failure of the given composite services in the execution time, including consistency of the functionality and QoS Criteria. This paper adopts Colored Petri Nets based model for Services Composition and proposes a QoS aware algorithm for verifying the consistency of composite services. A case study is provided for demonstrating the applicability of the proposed model and algorithm using concepts and values of QoS Criteria of composite services.

ACS Style

Hasan A.H Naji; Qingji Xue; Lingxiao Zhang; Ke Zheng. A Colored Petri Nets Based Model and Verification for Services Composition. Journal of Physics: Conference Series 2019, 1314, 012144 .

AMA Style

Hasan A.H Naji, Qingji Xue, Lingxiao Zhang, Ke Zheng. A Colored Petri Nets Based Model and Verification for Services Composition. Journal of Physics: Conference Series. 2019; 1314 (1):012144.

Chicago/Turabian Style

Hasan A.H Naji; Qingji Xue; Lingxiao Zhang; Ke Zheng. 2019. "A Colored Petri Nets Based Model and Verification for Services Composition." Journal of Physics: Conference Series 1314, no. 1: 012144.

Journal article
Published: 13 August 2018 in Sustainability
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With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.

ACS Style

Hasan. Naji; Qingji Xue; Nengchao Lyu; Chaozhong Wu; Ke Zheng. Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model. Sustainability 2018, 10, 2868 .

AMA Style

Hasan. Naji, Qingji Xue, Nengchao Lyu, Chaozhong Wu, Ke Zheng. Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model. Sustainability. 2018; 10 (8):2868.

Chicago/Turabian Style

Hasan. Naji; Qingji Xue; Nengchao Lyu; Chaozhong Wu; Ke Zheng. 2018. "Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model." Sustainability 10, no. 8: 2868.

Journal article
Published: 19 June 2017 in Information
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Taxi trajectories reflect human mobility over the urban roads’ network. Although taxi drivers cruise the same city streets, there is an observed variation in their daily profit. To reveal the reasons behind this issue, this study introduces a novel approach for investigating and understanding the impact of human mobility patterns (taxi drivers’ behavior) on daily drivers’ profit. Firstly, a K-means clustering method is adopted to group taxi drivers into three profitability groups according to their driving duration, driving distance and income. Secondly, the cruising trips and stopping spots for each profitability group are extracted. Thirdly, a comparison among the profitability groups in terms of spatial and temporal patterns on cruising trips and stopping spots is carried out. The comparison applied various methods including the mash map matching method and DBSCAN clustering method. Finally, an overall analysis of the results is discussed in detail. The results show that there is a significant relationship between human mobility patterns and taxi drivers’ profitability. High profitability drivers based on their experience earn more compared to other driver groups, as they know which places are more active to cruise and to stop and at what times. This study provides suggestions and insights for taxi companies and taxi drivers in order to increase their daily income and to enhance the efficiency of the taxi industry.

ACS Style

Hasan A. H. Naji; Chaozhong Wu; Hui Zhang. Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China. Information 2017, 8, 67 .

AMA Style

Hasan A. H. Naji, Chaozhong Wu, Hui Zhang. Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China. Information. 2017; 8 (2):67.

Chicago/Turabian Style

Hasan A. H. Naji; Chaozhong Wu; Hui Zhang. 2017. "Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China." Information 8, no. 2: 67.